1. Field of the Invention
The present invention relates to a method for determining the smoothness of fabric using image scanning.
2. Description of Related Art
In judging the aesthetic appeal of both apparel and household textiles, important concerns of consumers and researchers are focused on fabric wrinkle recovery and the wrinkle appearance after home laundering. Finding quantitative methods for evaluating wrinkling degrees of fabrics is one of the most important issues to researchers working in the field of fabric appearance retention and also to technicians assessing fabric characteristics. In the textile industry, currently there are two methods often used to grade wrinkles of a fabric. They are referred to as two AATCC Test Methods (TM): TM 124 and TM 128, each of which has one set of standard replicas used for comparison.
AATCC TM 128 is designed to rank the wrinkle degrees of textile fabrics after some wrinkles are induced. A test fabric specimen is wrinkled under a standard atmospheric condition in a standard wrinkling device. The device takes a predetermined load for a prescribed period of time to induce the fabric wrinkles. This specimen is then reconditioned and its appearance of wrinkles is evaluated. In this test method there are 5 standard replicas of different wrinkle degrees used for comparison.
For the standard test method of AATCC TM 124, standard procedures to rate wrinkles are specified. Under a standard lighting condition, three trained observers independently rate the wrinkling degree of a fabric specimen. They compare the appearance of the specimen with the smoothness of the appearances of six three-dimensional replicas. Each of the six replicas is made up of white plastic, representing a certain degree of wrinkling. This method is relatively simple and easy to perform. However, with the involvement of three trained technicians, it is very subjective in ranking wrinkling degrees of fabrics. Because of the subjectivity of the rating, one may question the credibility of the rating results: Are these wrinkle grades reliable? Are these grades repeatable or are these grades accurate? Actually one fabric might be ranked to belong to three different wrinkle levels by three observers, although these observers have been professionally trained to rank fabric wrinkles. In other words, grading results are usually biased by human perception. Sometimes it might be due to the subjective nature of human beings. Sometimes it might be due to the different regions attracting the attention of the three technician observers. Sometimes it might be due to the tilting angle when the fabric is held by observers during the rating processes.
Besides the questions on its reliability and accuracy, AATCC TM 124 is also expensive to execute. First, it needs at least three trained technicians. Second, this rating method should be performed in a standard lighting environment, which requires special rooms. Third, the standard replicas are expensive. In addition, a new set of replicas should be ordered and replaced every three years, because the combination of color aging and soiling from frequent usage may render the replicas unusable.
Therefore, it is desirable to develop a reliable, accurate, efficient and relatively economical system for automatically evaluating fabric wrinkles.
With the booming computer industry, image-processing techniques have been developed for various applications as well as for the textile industry. Image analysis has shown its potential application in objective evaluation of fabric appearances. It was reported that image-processing techniques have been developed to analyze fiber shape, yarn irregularity, and carpet appearance. It is suggested that image analysis is a good substitute for human inspection in fabric quality evaluation. Image analysis methods are increasingly adopted to obtain appearance characteristics because they are more repetitive, more reliable and more accurate than manual procedures. About eight years ago, a laser scanning system was developed to measure surface roughness and grade the pilling of fabrics. Computer image analysis techniques were designed to perform pill grading by matching the images of pilled fabrics with the images of a set of standard photographs representing various pilling appearances.
As to the evaluation of fabric wrinkles, some researchers have made contributions to the study of this field. As early as 1969, attempts were made to define the basic components of a wrinkle. Several descriptors were suggested, some of which provided a fair description of wrinkling such as wrinkle density, profile and randomness.
In 1995, fabric wrinkling was rated with intensity surface rations and shade area ratios by observing gray level intensity differences. In their approach, an HP color scanner was used so that an identical situation, such as illumination conditions and backgrounds, could be easily maintained. However, intensity images generated in this manner are not reliable indications. The features extracted from these images for wrinkle evaluation are not adequate to classify different kinds of fabric wrinkling, especially when the fabrics contain patterns or different colors.
Also considered were wrinkle density, profile, one-dimensional power spectrum density, sharpness, overall appearance and fractal dimension as feature parameters of fabric wrinkles. They were also using intensity images captured by a Sony CCD camera when replicas were illuminated from two comers by two light sources in a dark room. Based on the investigation and results, wrinkle surface ratio and standard deviation were suggested as feature parameters of wrinkle grades and a fractal theory was used to assess fabric wrinkles.
Using intensity images of the six standard replicas, the topographic labeling of the replicas using the facet model was analyzed. The surfaces of intensity gray level values were categorized into 11 labels. The percentages of the areas greater than and equal to 6, i.e., slopes, ridges, peaks, ravines, pits and saddles, were extracted as a feature to classify wrinkles on the replicas. Some interesting results were obtained concerning the relationship between grade numbers and the area percentages.
As mentioned above, all research on wrinkles was based on intensity images. Those techniques and algorithms suggested by researchers do not work properly to grade the wrinkling degrees of fabrics when fabrics have colored patterns.
Therefore, it is desirable to develop a new system that can objectively and accurately measure the smoothness of fabric appearances regardless of fabric patterns and colors. With laser-line projectors as light sources, such evaluation systems and algorithms have been developed with the help of image analysis. A laser-line triangulation method was used to measure the 3D surface of a wrinkled fabric with a 19-laser-line projector. A neural network was used to execute wrinkle classification with respect to the six standard replicas. Wrinkle parameters, such as arithmetic average roughness, root mean square roughness, 10-point height, and bearing-surface ratio, were fed into the neural network for training. In order to prevent or reduce the influence of wrinkle orientation, a rotating stage was required. In this system, some wrinkles of a fabric might be missed and some might be captured in a duplicated way. Recently, a stereovision algorithm was designed to evaluate fabric smoothness by fractal geometry. Because this method used two CCD cameras located at different angles together with two laser-line projectors over the detected objects, complicated calibrations are necessary to capture images of fabric wrinkles. Fractal dimension data were obtained as parameters of wrinkles, which fall between 2.015 and 2.035 for the 6 standard replicas.